Video surveillance systems are commonly used in combination with mapping applications in order to locate and track objects of interest within an area monitored by video cameras positioned at various locations within the area. In such an implementation, a target object is detected through video analytics processing, from which metadata are generated that relate to the location of the object relative to the view of a camera at which the object appears. This location is given using a coordinate system defined relative to the camera view. To facilitate mapping of the object, the view coordinates of the object are transformed into map coordinates, such as satellite positioning system (SPS) coordinates or the like.
Conventionally, the map location of an object is determined from image coordinates of the object associated with a camera at which the object appears by using 4-point or 9-point linear interpolation to derive the map coordinates from the image coordinates. However, these conventional linear interpolation techniques are associated with computationally difficult camera calibration procedures that reduce system efficiency. Further, linear interpolation of map coordinates associated with a given object from corresponding image coordinates using existing techniques often results in inaccurate map coordinates for the object.